What Is the Difference Between L1 (Lasso) and L2 (Ridge) Regularization Models?

Learn the key differences between L1 (Lasso) and L2 (Ridge) models, focusing on their regularization techniques and effects on coefficients.

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L1 model (Lasso) applies L1 regularization, which adds the absolute value of coefficients as a penalty term. This leads to sparse models by potentially driving some coefficients to zero. L2 model (Ridge) uses L2 regularization, adding the squared value of coefficients as a penalty term, which helps in reducing the magnitude of coefficients but doesn't make them zero, hence maintaining all features.

FAQs & Answers

  1. What is L1 regularization in machine learning? L1 regularization, also called Lasso, adds the absolute values of coefficients as a penalty to the loss function, encouraging sparsity by driving some coefficients to zero.
  2. How does L2 regularization differ from L1 regularization? L2 regularization, known as Ridge, adds the squared values of coefficients as a penalty, reducing their magnitude without eliminating any feature coefficients.
  3. When should I use L1 or L2 regularization? Use L1 regularization to create sparse models and perform feature selection, and use L2 when you want to reduce coefficient magnitude but retain all features in the model.
  4. Can L1 and L2 regularization be combined? Yes, combining L1 and L2 regularization is called Elastic Net, which balances feature selection and coefficient shrinkage benefits.

Watch More

For a deeper understanding of regularization techniques, explore our related videos on Elastic Net regularization and practical applications of Lasso and Ridge in model optimization.

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